Artificial neural network (ANN), in particular, convolutional neural network (CNN) has achieved great success in various fields. For example, in the field of computer vision (CV), CNN is widely used and most promising.
Image classification is a basic problem in computer vision (CV). In recent years, Convolutional Neural Network (CNN) has led to great advances in image classification accuracy. In Image-Net Large- Scale Vision Recognition Challenge (ILSVRC) 2012, Krizhevsky et al. showed that CNN had great power by achieving the top-5 accuracy of 84.7% in classification task, which was significantly higher than other traditional image classification methods. In the following years, the accuracy has been improved to 88.8%, 93.3%, and 96.4% in ILSVRC 2013, 2014, and 2015.
While achieving state-of-the-art performance, CNN-based methods demand much more computations and memory resources compared with traditional methods. In this manner, most CNN-based methods have to depend on large servers. However, there has been a non-negligible market for embedded systems which demands capabilities of high-accuracy and real-time object recognition, such as auto-piloted car and robots. But for embedded systems, the limited battery and resources are serious problems.
CNN is widely used in imaging processing. The advantages of CNN-based approach is that it is easy to train a CNN and CNN has a relatively simple computational structure. However, it is also known that CNN-based approach is both operational-intensive and memory-intensive. The existing CNNs commonly use 32-bit or 64-bit floating-number system. Given the fact that there are lots of redundant data in a CNN, it has been proposed to convert CNN into relatively short fixed-point numbers without substantial loss of accuracy.
In the previously filed patent applications, the inventor proposes a method and system to quantizing CNN into a fixed-point representation. For example, it proposes how to quantize a simple CNN (which has no complex sub-network/branches) layer by layer. Specifically, the proposed solution quantizes a serialized neural network. As shown in FIG. 2, in a serialized neural network, each layer N has only one preceding layer and one subsequent layer. The proposed solution aims to find an optimal fixed-point representation of each layer, starting from the input to the output of the neural network.
The above proposed solution cannot be applied to a complex network, such as GoogLeNet, SqueezeNet, etc., which have merged branches and parallel sub-networks.